A study funded by the National Institute of Mental Health employed the public-assess data from the MacArthur Violence Risk Assessment Study to develop violence risk assessment software, and also validated that software on independent samples of patients. The results of this validation study have been published:

Since that time, the dataset has been downloaded by over 500 people using
it for their own re-analyses.

Questions regarding the forthcoming availability of violence risk assessment
software developed under a National Institute of Health grant from the
publicly-available data set of the MacArthur Violence Risk Assessment Study
should be directed to John Monahan (jmonahan@virginia.edu).

Executive Summary

April 2001

Beliefs about the causes of mental disorder have shifted
over the centuries, but the belief that mental disorder predisposes many
of those suffering from it to behave violently has endured. Indeed, this
belief appears to have increased in intensity in the past several decades,
despite many educational campaigns designed to allay public apprehension.

These perceptions are reflected both in formal policies toward people
with mental disorders and in the public’s expectations about the
role of mental health professionals in insuring the safety of the community.
Violence risk assessment now is widely assumed by policy makers and the
public to be a core skill of the mental health professions and plays a
pivotal role in mental health law throughout the world. “Dangerousness
to others”
is now a principal standard for inpatient commitment, outpatient commitment,
and commitment to a forensic hospital. The imposition of tort liability
on mental health professionals who negligently fail to anticipate and
avert a patient’s violence to others has become commonplace.

Despite the pervasiveness of violence risk assessment in
mental health law, research continues to indicate that the unaided abilities
of mental health professionals to perform this task are modest at best.
Many have suggested that making available to clinicians statistical (“actuarial”)
information on the empirical relationships between various risk factors
and subsequent violent behavior is the only way to reduce the disconnect
between what the law demands and what clinicians currently are able to
provide.

General Research Strategy

The MacArthur Violence Risk Assessment Study[1] had two core goals: to do the best “science”
on violence risk assessment possible, and to produce an actuarial violence
risk assessment “tool” that clinicians in today’s
world of managed mental health services could actually use. From
these initial intellectual commitments, the Study evolved in six
stages over the decade it took to plan, execute, and analyze the
research.

Identifying Gaps in Methodology.
Almost all existing studies of violence risk assessment suffer from one
or more methodological problems: they considered a constricted
range of risk factors, often a few demographic variables or scores
on a psychological test; they employed weak criterion measures of violence,
usually relying solely on arrest; they studied a narrow segment of
the patient population, typically males with a history of prior violence;
and they were conducted at a single site. Based upon this critical
examination of existing work, we designed a piece of research that
could, to the greatest extent possible, overcome the methodological
obstacles we had identified.
We studied a large and diverse array of risk factors. We triangulated
our outcome measurement of violence, adding patient self-report and the
report of a collateral informant to data from official police and hospital
records. We studied both men and women, regardless of whether they
had a history of violence. And we conducted our study at several
sites rather than at a single site.

Selecting Promising Risk Factors.
Although we lacked any comprehensive theory of violence by
people with mental disorder from which we could derive hypothesized risk
factors, recent studies suggested that a number of variables might be
potent risk factors for violence among people with a mental disorder. We
assessed personal factors (e.g., demographic and personality variables),
historical factors (e.g., past violence and mental disorder), contextual
factors (e.g., social support and social networks), and clinical factors
(e.g., diagnosis and specific symptoms). We chose what we
believed to be the best of the existing measures of these variables,
and where no instrument to adequately measure a variable was available,
we commissioned the development of the necessary measure.

Using Tree-Based Methods. We
developed violence risk assessment models based on the “classification
tree” method rather than the usual linear regression method. A
classification tree approach reflects an interactive and contingent
model of violence, one that allows many different combinations of risk
factors to classify a person at a given level of risk. The particular
questions to be asked in any assessment grounded in this approach depend
on the answers given to prior questions. Factors that are relevant
to the risk assessment of one person may not be relevant to the risk
assessment of another person. This contrasts with a regression approach
in which a common set of questions is asked of everyone being assessed
and every answer is weighted to produce a score that can be used for
purposes of categorization.

Creating Different Cut-Offs for High and
Low Risk. Rather than relying on the standard single threshold
for distinguishing among cases, we decided to employ two thresholds –
one for identifying higher risk cases and one for identifying lower risk
cases. We assumed that inevitably there will be cases that fall
between these two thresholds, cases for which any actuarial prediction
scheme is incapable of making an adequate assessment of high or low risk. The
degree of risk presented by these intermediate cases cannot be statistically
distinguished from the baserate of the sample as a whole (therefore,
we refer to these cases as constituting an “average risk” group).

Repeating the Classification Tree.
To increase the predictive accuracy of a classification tree,
we re-analyzed those cases designated as “average risk”.
That is, all people not classified into groups designated as either high-
or low-risk in the standard classification tree model were pooled together
and re-analyzed. The logic here was that the people who were not
classified in the first iteration of the analysis might be different
in some significant ways from the people who were classified, and that
the full set of risk factors should be available to generate a new classification
tree specifically for these people who were not already classified as
high or low risk.
We referred to the resulting classification tree model as an “iterative”
classification tree).

Combining Multiple Risk Estimates.
Finally, we estimated several different risk assessment models in an attempt
to obtain multiple risk assessments for each case. That is, we chose
a number of different risk factors to be the lead variable upon which
a classification tree was constructed. In attempting to combine these
multiple risk estimates, we began to conceive of each separate risk estimate
as an indicator of the underlying construct of interest, violence risk.
The basic idea was that patients who scored in the high risk category on
many classification trees were more likely to be violent than patients
who scored in the high risk category on fewer classification trees. (And
analogously, patients who scored in the low risk category on many classification
trees were less likely to be violent than patients who scored in the low
risk category on fewer classification trees).

Specific Research Methods

Admissions (n=1,136) were sampled from acute civil inpatient
facilities in Pittsburgh, PA, Kansas City, MO, and Worcester, MA. We selected
English-speaking patients between the ages of 18 and 40, who were of White,
African American, or Hispanic ethnicity, and who had a chart diagnosis
of thought or affective disorder, substance abuse, or personality disorder.
The median length of stay was 9 days. After giving informed consent to
participate in the research, the patient was interviewed in the hospital
by both a research interviewer and a research clinician in order to assess
him or her on each of the risk factors.

Three sources of information were used to ascertain the occurrence and
details of a violent incident in the community. Interviews with
patients, interviews with collateral individuals (i.e., persons named
by the patient as someone who would know what was going on in his or
her life), and official sources of information (arrest and hospital records)
were all coded and compared. For the analyses reported here, the patients
and collaterals were interviewed twice (every 10 weeks) over the first
20 weeks -- approximately 4-5 months -- from the date of hospital discharge.

Violence to others was defined to included acts of battery that resulted
in physical injury; sexual assaults; assaultive acts that involved the
use of a weapon; or threats made with a weapon in hand.

Results

At least one violent act during the first 20 weeks after discharge from
the hospital was committed by 18.7 percent of the patients we studied.
Of the 134 risk factors we measured in the hospital, approximately half
(70) had a statistically significant bivariate relationship with later
violence in the community (p<.05). Some examples of specific risk factors
that were – or were not – significantly related to violence:


Gender.Men were somewhat more likely than women to be violent, but the
difference was not large. Violence by women was more likely than violence
by men to be directed against family members and to occur at home, and
less likely to result in medical treatment or arrest.

 Childhood experiences.
The seriousness and frequency of having been physically abused as a child
predicted subsequent violent behavior, as did having a parent – particularly
a father – who was a substance abuser or a criminal.

 Neighborhood
and race.While there was an overall association between
race and violence, African Americans and whites who lived in comparably
disadvantaged neighborhoods had the same rates of violence.


Diagnosis.A diagnosis of a major mental disorder -- especially a diagnosis
of schizophrenia -- was associated with a lower rate of violence than a diagnosis of a personality
or adjustment disorder. A co-occurring diagnosis of substance abuse was
strongly predictive of violence.

 Psychopathy.
Psychopathy, as measured by a screening version of the Hare Psychopathy
Checklist, was more strongly associated with violence than any other
risk factor we studied. The “antisocial behavior” component
of psychopathy, rather than the “emotional detachment” component,
accounted for most of this relationship.

 Delusions.The presence of delusions – or the type of delusions or the
content of delusions – was not associated with violence. A generally
“suspicious” attitude toward others was related to later violence.

 Hallucinations. Neither hallucinations in general, nor “command”
hallucinations per se, elevated the risk of violence. If voices specifically
commanded a violent act, however, the likelihood of violence was increased.


Violent thoughts.
Thinking or daydreaming about harming others was associated with violence,
particularly if the thoughts or daydreams were persistent.

 Anger. The higher a patient scored on the Novaco
Anger Scale in the hospital, the more likely he or she was to be violent
later in the community.

These are only bivariate relationships between risk factors
measured in the hospital and violence during the first 20 weeks after
discharge into the community, however. How do the risk factors perform
when combined in a multivariate way? Recall that we did not use regression-based
methods to combine variables, but rather employed the classification tree
technique.

Rather than pitting different tree-based risk assessment
models against one another and choosing the one model that appears
“best,” we used an approach that integrates the predictions
of many different risk assessment models, each of which may capture a
different but important facet of the interactive relationship between
the risk factors and violence. Using this approach, we ultimately combined
the results of five prediction models generated by the Iterative Classification
Tree methodology.
This combination of models produced results not only superior to those
of any of its constituent models, but superior to any other actuarial
violence risk assessment procedure reported in the literature to date.
Using only the 106 risk factors commonly available in hospital records
or capable of being routinely assessed in clinical practice, we were able
to place all patients into one of five risk classes for which the prevalence
of violence during the first 20 weeks following discharge into the community
varied between 1 percent and 76 percent. The risk factors that emerged
most frequently on the various models are presented in Table 1, and the
five risk groups that materialized from the use of these risk factors
-- along with the numbers of patients who fell into each risk group --
are presented in Figure 1.

Conclusions

The approach to risk assessment developed in the MacArthur Violence Risk
Assessment Study appears to be highly accurate when compared to other
approaches to assessing risk among people hospitalized in acute-care psychiatric
facilities. But it is also much more computationally complex than other
approaches. Five tree-based prediction models need to be constructed,
each involving the assessment of many risk factors. It would clearly be
impossible for a clinician to commit the multiple models and their scoring
to memory, since different risk factors are to be assessed for different
patients, and using a paper-and-pencil protocol would be very unwieldy.
Fortunately, however, the administration and scoring of multiple tree-based
models lends itself to software. In clinical use, the risk assessment
instrument we have developed would consist simply of a series of questions
that would flow one to the next on a computer screen -- through the various
iterations of each of the models as necessary -- depending on the answer
to each prior question.
Under a grant from the National Institute of Mental Health, we are currently
in the process of testing a prototype of such “violence risk assessment
software.” The software should be available in 2003. Further information
will be available at this website.

[1] For information on the MacArthur Community
Violence Study, which compared violence committed by persons discharged
from psychiatric facilities with violence committed by other people
living in the same neighborhoods, click here.